Rapid Detection of the Change in Surface Flow Patterns Near Fish Passages at Hydropower Dams With the Use of UAS Based Videos Under Controlled Discharge Conditions

The importance of keeping river environments healthy drives the scientific community towards the improvement of sustainable and validated environmental monitoring approaches. Accurate data on the state of the ecosystems provided rapidly are key in order to correctly assess, which interventions and management decisions are suitable, and which must be avoided. This paper analyses a rapid non-intrusive approach to change detection in surface flow patterns near fish passages at hydropower dams with the goal to improve the understanding of factors influencing fish passage discoverability. This, in turn, is of great relevance to the sustainability of migrating riverine fish populations from both ecological and economical perspectives. The present study includes three unique experiments performed at a large-scale hydropower dam site with an integrated fish passage under controlled discharge conditions. The analysis is performed with the use of the freely available KLT-IV software. The use of an Unmanned Aerial System (UAS) as a camera carrier platform provides the key flexibility in terms of any study site selection. The use of KLT-IV speeds up and simplifies flow pattern analysis, especially when compared to labour-intensive modelling relying on point-based ground truth data. In this paper, we demonstrate that the selected approach can be effectively applied to identify changes in surface flow patterns both in terms of flow velocity magnitudes and in terms of flow directions. It shows that the identification of actual flow patterns near the fish passage entrance provides more information on the potential discoverability of the fish passage than traditionally measured bulk discharge values alone

Metabolomics Unravel Contrasting Effects of Biodiversity on the Performance of Individual Plant Species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment

Tumour brain: pre‐treatment cognitive and affective disorders caused by peripheral cancers

People that develop extracranial cancers often display co-morbid neurological disorders, such as anxiety, depression and cognitive impairment, even before commencement of chemotherapy. This suggests bidirectional crosstalk between non-CNS tumours and the brain, which can regulate peripheral tumour growth. However, the reciprocal neurological effects of tumour progression on brain homeostasis are not well understood. Here, we review brain regions involved in regulating peripheral tumour development and how they, in turn, are adversely affected by advancing tumour burden. Tumour-induced activation of the immune system, blood–brain barrier breakdown and chronic neuroinflammation can lead to circadian rhythm dysfunction, sleep disturbances, aberrant glucocorticoid production, decreased hippocampal neurogenesis and dysregulation of neural network activity, resulting in depression and memory impairments. Given that cancer-related cognitive impairment diminishes patient quality of life, reduces adherence to chemotherapy and worsens cancer prognosis, it is essential that more research is focused at understanding how peripheral tumours affect brain homeostasis

Neural correlates of processing stressful information: An event-related fMRI study

Recent neuroimaging studies investigating neural correlates of psychological stress employ cognitive paradigms that induce a significant hormonal stress response in the scanner. The Montreal Imaging Stress Task (MIST) is one such task that combines challenging mental arithmetic with negative social evaluative feedback. Due to the block design nature of the MIST, it has not been possible thus far to investigate which brain areas respond specifically to the key components of the MIST (mental arithmetic, failure, negative social evaluation). In the current study, we developed an event-related MIST (eventMIST) in order to investigate which neural activation patterns are associated with performing mental arithmetic vs. processing of social evaluative threat. Data was available from twenty healthy university students. The eventMIST induced a significant stress response in a subsample of subjects, called the responders (n = 7). Direct comparison between brain activity changes in responders vs. non-responders, in response to challenging math, revealed increased activity bilaterally in dorsomedial prefrontal cortex (PFC), left temporal pole, and right dorsolateral PFC. In response to negative social evaluation, responders showed reduction of brain activity in limbic system regions (medial orbitofrontal cortex and hippocampus), which was largely lacking in non-responders. Direct comparison between the groups for this contrast did not reveal any significant difference, probably due to small number of events available. This is the first study to use an event-related paradigm to investigate brain activity patterns in relation to challenging math and social evaluative threat separately

A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos-in the form of static features-to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools-six aimed specifically at velocimetry and one general purpose software-were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion